Payload fragmentation framework for high-performance computing in cloud environment.
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| Title: | Payload fragmentation framework for high-performance computing in cloud environment. |
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| Authors: | Srinivasan, R.1, Elijah Blessing, R.1, Vivek, V.1,2, Dhanasekaran, R.3 |
| Source: | Journal of Supercomputing. May2019, Vol. 75 Issue 5, p2789-2804. 16p. |
| Subjects: | Cloud computing, Storage fragmentation (Computer science), Central processing units, C++, Compilers (Computer programs) |
| Abstract: | Cloud computing environment delivers resources such as CPU cycles, storage, network service and memory through a web-based model. Users run their applications on remote infrastructure and get benefited more at a lesser cost. Compiling large-scale applications such as compiling GCC and compiling Clang in C and C++ environments where applications involve encryption breaking tools require extensive analysis. Lack of CPU cycles in consumer-grade computing will be detrimental to such applications. Fragmenting and distributing payloads across multiple clusters for such applications in a cloud-like environment is a challenging task. We have proposed a novel approach to perform payload distribution for users who want to run their computationally expensive tasks efficiently. The proposed framework has performed well compared to the 'traditional' payload execution policy. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Cloud computing environment delivers resources such as CPU cycles, storage, network service and memory through a web-based model. Users run their applications on remote infrastructure and get benefited more at a lesser cost. Compiling large-scale applications such as compiling GCC and compiling Clang in C and C++ environments where applications involve encryption breaking tools require extensive analysis. Lack of CPU cycles in consumer-grade computing will be detrimental to such applications. Fragmenting and distributing payloads across multiple clusters for such applications in a cloud-like environment is a challenging task. We have proposed a novel approach to perform payload distribution for users who want to run their computationally expensive tasks efficiently. The proposed framework has performed well compared to the 'traditional' payload execution policy. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 09208542 |
| DOI: | 10.1007/s11227-018-2660-7 |